Question 0 -- Practice with Drag & Drop
0 points possible (ungraded)
Keyboard Help
11/11/2019 Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first before taking the Midterm Quiz 1) | Step 2: Midterm Quiz
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Question 0 -- Practice with Drag & Drop
0 points possible (ungraded)
Keyboard Help
11/11/2019 Step 2: Midterm Quiz 1 - GT Students (Launch Proctortrack first before taking the Midterm Quiz 1) | Step 2: Midterm Quiz 1 - GT St…
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Some of the quiz questions are Drag-and-Drop. You'll need to drag one or more answers
to a location.
Some answers might not be used at all, and some answers will be used once. To get full
credit you might need to drag more than one answer to some locations, just one answer
to other locations, and some locations might not have any correct answers.
Please do this quick practice question. The question will give you feedback to make sure
you've done it correctly, but the real quiz questions will not.
FEEDBACK
Correctly placed 3 items.
Good work! You have completed this drag and drop problem. Note that: (1) There are two places
you could've put (x=2,y=3); either one would be correct. (2) One location (x+y=2) had nothing
dragged to it. Another location had two answers dragged to it. (3) One choice (x=1,y=7) was not
dragged anywhere, since it wasn't correct for anything.
You have used 6 of 10 attempts.
Reset
Submit
Show Answer Question 1
9/13 points (graded)
Keyboard Help
x=1, y=7
x=2, y=3 x=1, y=4
x=1, y=6
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Drag each model or method to a category of question it is commonly used for. For
models/methods that have more than one correct category, choose any one correct
category; for models/methods that have no correct category listed, do not drag them.
FEEDBACK
Correctly placed 8 items.
Misplaced 1 item. Submit
You have used 1 of 1 attempts.
Reset
Show Answer
CUSUM Principal component
analysis
Support vector machine k-means ARIMA CART Exponential smoothing k-nearest-neighbor Linear regression Logistic regression Random forest Cross validation GARCH
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Question 2
2.19/3.0 points (graded)
Select all of the following models that are designed for use with time series data:
You have used 1 of 1 attempt
Did not place 3 required items.
Good work! You have completed this drag and drop problem.
Final attempt was used, highest score is 9.0
k-nearest-neighbor
Principal component analysis
ARIMA
k-means
CUSUM
Logistic regression
GARCH
Exponential smoothing
Random forest
Linear regression
Support vector machine
Submit
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Answers are displayed within the problem
Information for Questions 3a, 3b, 3c
Figures A and B show the training data for a soft classication problem,
using two predictors (x and x ) to separate between black and white
points. The dashed lines are the classiers found using SVM. Figure A
uses a linear kernel, and Figure B uses a nonlinear kernel that required
tting 16 parameter values.
Figure A Figure B
Question 3a
2.4/3.0 points (graded)
3a. Select all of the following statements that are true.
1 2
Figure A's classier is based only on the value of x2 .
Figure A has fewer classication errors in the training data.
Figure A's classier has a wider margin in the training data.
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Answers are displayed within the problem
Answers are displayed within the problem
You have used 1 of 1 attempt
Question 3b
2.25/3.0 points (graded)
3b. Select all of the following statements that are true.
You have used 1 of 1 attempt
Question 3c
1.5/3.0 points (graded)
3c. Select all of the following statements that are true.
Figure A's classier incorrectly classies exactly 4 white points in the training data.
Figure A shows that the black point (7.2,1.4) is an outlier.
Submit
Figure B's classier has a narrower margin in the training data.
Figure B's classier is more likely to be over-t.
Figure B's classier incorrectly classies exactly 5 white points in the training data.
Figure B shows that the black point (7.2,1.4) should be white.
Submit
A new point at (3,3) would be classied as white by Figure A's classier.
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